Automatic Response Category Combination in Multinomial Logistic Regression

Bradley S. Price, Charles J Geyer, Adam J Rothman

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is nondifferentiable when pairs of columns in the optimization variable are equal. This encourages pairwise equality of these columns in the estimator, which corresponds to response category combination. We use an alternating direction method of multipliers algorithm to compute the estimator and we discuss the algorithm’s convergence. Prediction and model selection are also addressed. Supplemental materials for this article are available online.

Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
DOIs
StatePublished - Jan 1 2019

Fingerprint

Logistic Regression
Method of multipliers
Estimator
Alternating Direction Method
Penalized Likelihood
Logistic Regression Model
Likelihood Methods
Model Selection
Penalty
Pairwise
Equality
Subset
Optimization
Prediction
Multinomial logistic regression
Multiplier
Logistic regression model
Model selection

Keywords

  • Fusion penalty
  • Multinomial logistic regression
  • Response category reduction

Cite this

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